BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909232524165120 |
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| author | Benfenati, Luca Ingolfsson, Thorir Mar Cossettini, Andrea Pagliari, Daniele Jahier Burrello, Alessio Benini, Luca |
| author_facet | Benfenati, Luca Ingolfsson, Thorir Mar Cossettini, Andrea Pagliari, Daniele Jahier Burrello, Alessio Benini, Luca |
| contents | This study presents a novel approach for EEG-based seizure detection leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training process. Initially, it is pre-trained on the extensive Temple University Hospital EEG Corpus (TUEG), a 1.5 TB dataset comprising over 10,000 subjects, to extract common EEG data patterns. Subsequently, the model is fine-tuned on the CHB-MIT Scalp EEG Database, consisting of 664 EEG recordings from 24 pediatric patients, of which 198 contain seizure events. Key contributions include optimizing fine-tuning on the CHB-MIT dataset, where the impact of model architecture, pre-processing, and post-processing techniques are thoroughly examined to enhance sensitivity and reduce false positives per hour (FP/h). We also explored custom training strategies to ascertain the most effective setup. The model undergoes a novel second pre-training phase before subject-specific fine-tuning, enhancing its generalization capabilities. The optimized model demonstrates substantial performance enhancements, achieving as low as 0.23 FP/h, 2.5$\times$ lower than the baseline model, with a lower but still acceptable sensitivity rate, showcasing the effectiveness of applying a BERT-based approach on EEG-based seizure detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19189 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring Benfenati, Luca Ingolfsson, Thorir Mar Cossettini, Andrea Pagliari, Daniele Jahier Burrello, Alessio Benini, Luca Machine Learning Artificial Intelligence This study presents a novel approach for EEG-based seizure detection leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training process. Initially, it is pre-trained on the extensive Temple University Hospital EEG Corpus (TUEG), a 1.5 TB dataset comprising over 10,000 subjects, to extract common EEG data patterns. Subsequently, the model is fine-tuned on the CHB-MIT Scalp EEG Database, consisting of 664 EEG recordings from 24 pediatric patients, of which 198 contain seizure events. Key contributions include optimizing fine-tuning on the CHB-MIT dataset, where the impact of model architecture, pre-processing, and post-processing techniques are thoroughly examined to enhance sensitivity and reduce false positives per hour (FP/h). We also explored custom training strategies to ascertain the most effective setup. The model undergoes a novel second pre-training phase before subject-specific fine-tuning, enhancing its generalization capabilities. The optimized model demonstrates substantial performance enhancements, achieving as low as 0.23 FP/h, 2.5$\times$ lower than the baseline model, with a lower but still acceptable sensitivity rate, showcasing the effectiveness of applying a BERT-based approach on EEG-based seizure detection. |
| title | BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.19189 |